Overview

Dataset statistics

Number of variables10
Number of observations419403
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.0 MiB
Average record size in memory80.0 B

Variable types

Numeric10

Warnings

Date is highly correlated with Biomass_MWHigh correlation
Consumption_MW is highly correlated with Coal_MW and 2 other fieldsHigh correlation
Coal_MW is highly correlated with Consumption_MWHigh correlation
Gas_MW is highly correlated with Consumption_MW and 1 other fieldsHigh correlation
Biomass_MW is highly correlated with DateHigh correlation
Production_MW is highly correlated with Consumption_MW and 1 other fieldsHigh correlation
Date is highly correlated with Wind_MW and 1 other fieldsHigh correlation
Consumption_MW is highly correlated with Coal_MW and 2 other fieldsHigh correlation
Coal_MW is highly correlated with Consumption_MWHigh correlation
Gas_MW is highly correlated with Consumption_MW and 1 other fieldsHigh correlation
Wind_MW is highly correlated with DateHigh correlation
Biomass_MW is highly correlated with DateHigh correlation
Production_MW is highly correlated with Consumption_MW and 1 other fieldsHigh correlation
Date is highly correlated with Biomass_MWHigh correlation
Consumption_MW is highly correlated with Production_MWHigh correlation
Biomass_MW is highly correlated with DateHigh correlation
Production_MW is highly correlated with Consumption_MWHigh correlation
Consumption_MW is highly correlated with Coal_MW and 1 other fieldsHigh correlation
Biomass_MW is highly correlated with DateHigh correlation
Coal_MW is highly correlated with Consumption_MW and 1 other fieldsHigh correlation
Date is highly correlated with Biomass_MW and 1 other fieldsHigh correlation
Production_MW is highly correlated with Consumption_MW and 1 other fieldsHigh correlation
Wind_MW is highly correlated with DateHigh correlation
Wind_MW has 27315 (6.5%) zeros Zeros
Solar_MW has 219493 (52.3%) zeros Zeros
Biomass_MW has 183783 (43.8%) zeros Zeros

Reproduction

Analysis started2021-09-09 22:52:21.885659
Analysis finished2021-09-09 22:52:41.447349
Duration19.56 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Date
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct419398
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1390064137
Minimum1262487660
Maximum1514947775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2021-09-09T18:52:41.503648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1262487660
5-th percentile1275541800
Q11327113841
median1391037353
Q31453085107
95-th percentile1502572105
Maximum1514947775
Range252460115
Interquartile range (IQR)125971266

Descriptive statistics

Standard deviation72778786.87
Coefficient of variation (CV)0.05235642366
Kurtosis-1.197368969
Mean1390064137
Median Absolute Deviation (MAD)62977612
Skewness-0.02476660488
Sum5.829970692 × 1014
Variance5.296751818 × 1015
MonotonicityIncreasing
2021-09-09T18:52:41.674006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12685367402
 
< 0.1%
12685379402
 
< 0.1%
12685373402
 
< 0.1%
12685384802
 
< 0.1%
12685360802
 
< 0.1%
14407495651
 
< 0.1%
14167015171
 
< 0.1%
14644986581
 
< 0.1%
13260743401
 
< 0.1%
14131102461
 
< 0.1%
Other values (419388)419388
> 99.9%
ValueCountFrequency (%)
12624876601
< 0.1%
12624882001
< 0.1%
12624888001
< 0.1%
12624894001
< 0.1%
12624900601
< 0.1%
12624906601
< 0.1%
12624912601
< 0.1%
12624924001
< 0.1%
12624930001
< 0.1%
12624936601
< 0.1%
ValueCountFrequency (%)
15149477751
< 0.1%
15149471851
< 0.1%
15149465951
< 0.1%
15149460051
< 0.1%
15149454151
< 0.1%
15149448251
< 0.1%
15149442351
< 0.1%
15149436451
< 0.1%
15149430551
< 0.1%
15149424651
< 0.1%

Consumption_MW
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5515
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6608.54512
Minimum44
Maximum26209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2021-09-09T18:52:41.747386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile5078
Q15834
median6578
Q37279
95-th percentile8388
Maximum26209
Range26165
Interquartile range (IQR)1445

Descriptive statistics

Standard deviation1007.541019
Coefficient of variation (CV)0.1524603374
Kurtosis-0.04993645558
Mean6608.54512
Median Absolute Deviation (MAD)724
Skewness0.2620643423
Sum2771643649
Variance1015138.904
MonotonicityNot monotonic
2021-09-09T18:52:41.820689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6588347
 
0.1%
6482255
 
0.1%
6722215
 
0.1%
6686203
 
< 0.1%
6727202
 
< 0.1%
6795202
 
< 0.1%
6542201
 
< 0.1%
6581201
 
< 0.1%
6770199
 
< 0.1%
6578198
 
< 0.1%
Other values (5505)417180
99.5%
ValueCountFrequency (%)
441
< 0.1%
471
< 0.1%
941
< 0.1%
951
< 0.1%
36661
< 0.1%
36671
< 0.1%
36981
< 0.1%
37091
< 0.1%
37131
< 0.1%
37142
< 0.1%
ValueCountFrequency (%)
262091
< 0.1%
210071
< 0.1%
98651
< 0.1%
98261
< 0.1%
98071
< 0.1%
97841
< 0.1%
97661
< 0.1%
97391
< 0.1%
97291
< 0.1%
97211
< 0.1%

Coal_MW
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3840
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2257.775364
Minimum-485
Maximum5702
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size3.2 MiB
2021-09-09T18:52:41.891750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-485
5-th percentile1346
Q11834
median2196
Q32652.5
95-th percentile3340
Maximum5702
Range6187
Interquartile range (IQR)818.5

Descriptive statistics

Standard deviation610.8365915
Coefficient of variation (CV)0.2705479922
Kurtosis-0.08130149096
Mean2257.775364
Median Absolute Deviation (MAD)403
Skewness0.309782812
Sum946917761
Variance373121.3415
MonotonicityNot monotonic
2021-09-09T18:52:41.964174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2593395
 
0.1%
2088344
 
0.1%
2040342
 
0.1%
2073340
 
0.1%
1917338
 
0.1%
2150335
 
0.1%
2001334
 
0.1%
2134334
 
0.1%
2105333
 
0.1%
2081333
 
0.1%
Other values (3830)415975
99.2%
ValueCountFrequency (%)
-4851
 
< 0.1%
-431
 
< 0.1%
502
< 0.1%
3571
 
< 0.1%
3584
< 0.1%
3594
< 0.1%
3602
< 0.1%
3623
< 0.1%
3644
< 0.1%
3652
< 0.1%
ValueCountFrequency (%)
57021
< 0.1%
53381
< 0.1%
44081
< 0.1%
43951
< 0.1%
43831
< 0.1%
43701
< 0.1%
43691
< 0.1%
43681
< 0.1%
43651
< 0.1%
43531
< 0.1%

Gas_MW
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct2300
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1008.151282
Minimum-414
Maximum2666
Zeros2
Zeros (%)< 0.1%
Negative1
Negative (%)< 0.1%
Memory size3.2 MiB
2021-09-09T18:52:42.038209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-414
5-th percentile366
Q1604
median984
Q31329
95-th percentile1871
Maximum2666
Range3080
Interquartile range (IQR)725

Descriptive statistics

Standard deviation469.6586672
Coefficient of variation (CV)0.4658613005
Kurtosis-0.5246681608
Mean1008.151282
Median Absolute Deviation (MAD)365
Skewness0.4147662848
Sum422821672
Variance220579.2637
MonotonicityNot monotonic
2021-09-09T18:52:42.107358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
416778
 
0.2%
417702
 
0.2%
415638
 
0.2%
400592
 
0.1%
418576
 
0.1%
398569
 
0.1%
397567
 
0.1%
424564
 
0.1%
399556
 
0.1%
423553
 
0.1%
Other values (2290)413308
98.5%
ValueCountFrequency (%)
-4141
 
< 0.1%
02
 
< 0.1%
291
 
< 0.1%
331
 
< 0.1%
1161
 
< 0.1%
1209
< 0.1%
1212
 
< 0.1%
1281
 
< 0.1%
1298
< 0.1%
13013
< 0.1%
ValueCountFrequency (%)
26661
< 0.1%
26601
< 0.1%
26591
< 0.1%
26511
< 0.1%
25061
< 0.1%
25042
< 0.1%
25011
< 0.1%
24991
< 0.1%
24631
< 0.1%
24582
< 0.1%

Hidroelectric_MW
Real number (ℝ≥0)

Distinct4221
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1840.614073
Minimum0
Maximum4728
Zeros21
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2021-09-09T18:52:42.180174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile669
Q11262
median1809
Q32395
95-th percentile3100
Maximum4728
Range4728
Interquartile range (IQR)1133

Descriptive statistics

Standard deviation754.0300395
Coefficient of variation (CV)0.4096622158
Kurtosis-0.564910179
Mean1840.614073
Median Absolute Deviation (MAD)566
Skewness0.1825489605
Sum771959064
Variance568561.3005
MonotonicityNot monotonic
2021-09-09T18:52:42.252792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2476359
 
0.1%
1550239
 
0.1%
1573238
 
0.1%
2012232
 
0.1%
1621229
 
0.1%
1913229
 
0.1%
1435228
 
0.1%
1505228
 
0.1%
1649227
 
0.1%
1384226
 
0.1%
Other values (4211)416968
99.4%
ValueCountFrequency (%)
021
< 0.1%
531
 
< 0.1%
561
 
< 0.1%
581
 
< 0.1%
592
 
< 0.1%
603
 
< 0.1%
851
 
< 0.1%
872
 
< 0.1%
901
 
< 0.1%
911
 
< 0.1%
ValueCountFrequency (%)
47281
< 0.1%
47061
< 0.1%
47001
< 0.1%
46921
< 0.1%
46871
< 0.1%
46801
< 0.1%
46681
< 0.1%
46641
< 0.1%
46631
< 0.1%
46561
< 0.1%

Nuclear_MW
Real number (ℝ≥0)

Distinct879
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1320.407794
Minimum0
Maximum1450
Zeros129
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2021-09-09T18:52:42.325000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile702
Q11377
median1403
Q31419
95-th percentile1427
Maximum1450
Range1450
Interquartile range (IQR)42

Descriptive statistics

Standard deviation223.1816069
Coefficient of variation (CV)0.1690247573
Kurtosis4.082766423
Mean1320.407794
Median Absolute Deviation (MAD)19
Skewness-2.410152883
Sum553782990
Variance49810.02967
MonotonicityNot monotonic
2021-09-09T18:52:42.398534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142111618
 
2.8%
142211318
 
2.7%
142311129
 
2.7%
142011038
 
2.6%
141910593
 
2.5%
142410450
 
2.5%
14189729
 
2.3%
14259619
 
2.3%
14178851
 
2.1%
14268417
 
2.0%
Other values (869)316641
75.5%
ValueCountFrequency (%)
0129
< 0.1%
371
 
< 0.1%
394
 
< 0.1%
403
 
< 0.1%
441
 
< 0.1%
451
 
< 0.1%
471
 
< 0.1%
491
 
< 0.1%
701
 
< 0.1%
831
 
< 0.1%
ValueCountFrequency (%)
14501
 
< 0.1%
14431
 
< 0.1%
14413
 
< 0.1%
144011
 
< 0.1%
143913
 
< 0.1%
143829
 
< 0.1%
143752
 
< 0.1%
1436113
 
< 0.1%
1435252
0.1%
1434374
0.1%

Wind_MW
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2828
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501.8796504
Minimum-521
Maximum7944
Zeros27315
Zeros (%)6.5%
Negative11515
Negative (%)2.7%
Memory size3.2 MiB
2021-09-09T18:52:42.478034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-521
5-th percentile0
Q164
median272
Q3733
95-th percentile1804
Maximum7944
Range8465
Interquartile range (IQR)669

Descriptive statistics

Standard deviation585.6012027
Coefficient of variation (CV)1.166815993
Kurtosis1.764636182
Mean501.8796504
Median Absolute Deviation (MAD)247
Skewness1.517715362
Sum210489831
Variance342928.7686
MonotonicityNot monotonic
2021-09-09T18:52:42.549140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
027315
 
6.5%
-13060
 
0.7%
-22222
 
0.5%
31655
 
0.4%
11559
 
0.4%
21531
 
0.4%
51390
 
0.3%
41383
 
0.3%
-31379
 
0.3%
61332
 
0.3%
Other values (2818)376577
89.8%
ValueCountFrequency (%)
-5212
 
< 0.1%
-263
 
< 0.1%
-2518
 
< 0.1%
-2432
 
< 0.1%
-2334
< 0.1%
-2257
< 0.1%
-2141
< 0.1%
-2054
< 0.1%
-1952
< 0.1%
-1884
< 0.1%
ValueCountFrequency (%)
79441
< 0.1%
28061
< 0.1%
28031
< 0.1%
28021
< 0.1%
28002
< 0.1%
27991
< 0.1%
27982
< 0.1%
27971
< 0.1%
27961
< 0.1%
27951
< 0.1%

Solar_MW
Real number (ℝ)

ZEROS

Distinct857
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.44516849
Minimum-6
Maximum859
Zeros219493
Zeros (%)52.3%
Negative78275
Negative (%)18.7%
Memory size3.2 MiB
2021-09-09T18:52:42.624790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile-1
Q10
median0
Q316
95-th percentile492
Maximum859
Range865
Interquartile range (IQR)16

Descriptive statistics

Standard deviation161.3681006
Coefficient of variation (CV)2.290690817
Kurtosis5.507535705
Mean70.44516849
Median Absolute Deviation (MAD)0
Skewness2.512139247
Sum29544915
Variance26039.66388
MonotonicityNot monotonic
2021-09-09T18:52:42.694096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0219493
52.3%
-178214
 
18.6%
13468
 
0.8%
21704
 
0.4%
31322
 
0.3%
41189
 
0.3%
51011
 
0.2%
6915
 
0.2%
7912
 
0.2%
8854
 
0.2%
Other values (847)110321
26.3%
ValueCountFrequency (%)
-61
 
< 0.1%
-48
 
< 0.1%
-341
 
< 0.1%
-211
 
< 0.1%
-178214
 
18.6%
0219493
52.3%
13468
 
0.8%
21704
 
0.4%
31322
 
0.3%
41189
 
0.3%
ValueCountFrequency (%)
8591
< 0.1%
8581
< 0.1%
8541
< 0.1%
8522
< 0.1%
8511
< 0.1%
8481
< 0.1%
8471
< 0.1%
8451
< 0.1%
8441
< 0.1%
8431
< 0.1%

Biomass_MW
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.36559347
Minimum0
Maximum110
Zeros183783
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2021-09-09T18:52:42.766992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median33
Q355
95-th percentile67
Maximum110
Range110
Interquartile range (IQR)55

Descriptive statistics

Standard deviation27.42416309
Coefficient of variation (CV)0.9338875821
Kurtosis-1.713833507
Mean29.36559347
Median Absolute Deviation (MAD)33
Skewness0.03615239414
Sum12316018
Variance752.0847211
MonotonicityNot monotonic
2021-09-09T18:52:42.838276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0183783
43.8%
3314475
 
3.5%
579318
 
2.2%
529217
 
2.2%
559201
 
2.2%
589046
 
2.2%
618868
 
2.1%
647630
 
1.8%
567551
 
1.8%
537520
 
1.8%
Other values (91)152794
36.4%
ValueCountFrequency (%)
0183783
43.8%
103
 
< 0.1%
1133
 
< 0.1%
1272
 
< 0.1%
1314
 
< 0.1%
1426
 
< 0.1%
1513
 
< 0.1%
16104
 
< 0.1%
17230
 
0.1%
1868
 
< 0.1%
ValueCountFrequency (%)
1104
 
< 0.1%
10914
< 0.1%
10811
< 0.1%
10711
< 0.1%
1063
 
< 0.1%
1055
 
< 0.1%
1043
 
< 0.1%
1033
 
< 0.1%
1023
 
< 0.1%
1001
 
< 0.1%

Production_MW
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6936
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7028.770679
Minimum0
Maximum11295
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2021-09-09T18:52:42.913022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5208
Q16177
median6973
Q37820
95-th percentile9058
Maximum11295
Range11295
Interquartile range (IQR)1643

Descriptive statistics

Standard deviation1169.322621
Coefficient of variation (CV)0.1663623235
Kurtosis-0.3185947843
Mean7028.770679
Median Absolute Deviation (MAD)821
Skewness0.2301559273
Sum2947887509
Variance1367315.393
MonotonicityNot monotonic
2021-09-09T18:52:42.984073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7001322
 
0.1%
7066252
 
0.1%
6697177
 
< 0.1%
6840176
 
< 0.1%
6696174
 
< 0.1%
6654174
 
< 0.1%
6673169
 
< 0.1%
6732169
 
< 0.1%
6734168
 
< 0.1%
6566167
 
< 0.1%
Other values (6926)417455
99.5%
ValueCountFrequency (%)
01
< 0.1%
441
< 0.1%
471
< 0.1%
501
< 0.1%
7441
< 0.1%
9361
< 0.1%
36161
< 0.1%
36211
< 0.1%
36711
< 0.1%
36751
< 0.1%
ValueCountFrequency (%)
112951
< 0.1%
112271
< 0.1%
112191
< 0.1%
112051
< 0.1%
111831
< 0.1%
111531
< 0.1%
111501
< 0.1%
111461
< 0.1%
111381
< 0.1%
111311
< 0.1%

Interactions

2021-09-09T18:52:30.045491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:30.172800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:30.284037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:30.407539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:30.520264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:30.706619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:30.817827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:30.924653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:31.031960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:31.142017image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:31.252729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:31.356602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:31.460721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:31.565163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:31.673996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:31.775517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:31.880412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:31.980940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:32.079969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:32.182260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:32.287559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:32.391616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:32.496192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:32.600600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:32.707647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:32.810062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:32.916223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:33.018318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:33.118945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:33.222894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:33.329714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:33.433334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:33.592126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:33.697337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:33.804251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:33.906101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:34.011345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:34.113883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:34.214351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:34.317659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:34.425901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:34.526682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:34.627792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:34.730707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:34.833766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:34.933354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:35.035985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:35.134919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:35.232770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:35.333662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:35.436567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:35.543314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:35.649626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:35.756697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:35.867375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:35.972427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:36.081483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:36.185338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:36.288494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:36.394673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:36.503497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:36.603064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:36.703326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:36.804332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:36.907248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:37.075377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:37.176837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:37.273976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:37.369792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:37.469329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:37.570737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:37.670041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:37.771043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:37.870962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:37.973232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:38.070645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:38.172109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:38.269117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:38.367027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:38.466768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:38.569206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:38.671267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:38.773113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:38.875339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:38.979821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:39.079512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:39.183700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:39.283449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:39.382652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:39.485378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:39.591262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:39.694858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:39.798019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:39.902846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:40.009367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:40.109949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:40.214537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:40.315232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:40.415221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-09T18:52:40.517384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-09-09T18:52:43.051153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-09T18:52:43.148987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-09T18:52:43.244529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-09T18:52:43.341512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-09-09T18:52:40.636043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-09T18:52:40.947316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateConsumption_MWCoal_MWGas_MWHidroelectric_MWNuclear_MWWind_MWSolar_MWBiomass_MWProduction_MW
012624876605302.01754.01144.01391.0706.00.00.00.04995.0
112624882005318.01777.01145.01468.0708.00.00.00.05097.0
212624888005268.01743.01139.01361.0708.00.00.00.04951.0
312624894005358.01759.01142.01449.0707.00.00.00.05057.0
412624900605327.01764.01142.01417.0709.00.00.00.05031.0
512624906605307.01771.01142.01418.0706.00.00.00.05037.0
612624912605256.01752.01153.01368.0712.00.00.00.04985.0
712624924005308.01762.01151.01461.0709.00.00.00.05083.0
812624930005426.01785.01153.01515.0704.00.00.00.05157.0
912624936605340.01782.01150.01488.0706.00.00.00.05126.0

Last rows

DateConsumption_MWCoal_MWGas_MWHidroelectric_MWNuclear_MWWind_MWSolar_MWBiomass_MWProduction_MW
41939315149424657307.02328.0955.01311.01404.02269.0-1.043.08308.0
41939415149430557295.02250.0941.01335.01404.02244.0-1.044.08217.0
41939515149436457272.02253.0946.01395.01404.02205.0-1.045.08246.0
41939615149442357266.02239.0946.01387.01406.02204.0-1.044.08224.0
41939715149448257287.02275.0945.01425.01401.02193.0-1.045.08283.0
41939815149454157262.02279.0942.01444.01403.02175.0-1.045.08287.0
41939915149460057167.02259.0943.01383.01405.02174.0-1.043.08207.0
41940015149465957122.02251.0945.01362.01405.02159.0-1.045.08165.0
41940115149471857264.02288.0944.01454.01406.02132.0-1.045.08268.0
41940215149477757115.02255.0944.01370.01404.02131.0-1.041.08145.0